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As is known, a cellular neural network is a system consisting of elementary cells operating in continuous time, equipped with a eral state variable connected with the neighboring cells over a short distance in one-, two-, or three dimensional space. This system can be considered a programmable parallel, analog processor capable in particular of a wide range of applications in the image processing field. It can be made self-adaptive with the addition of the appropriate circuits.
The type of processing realized by a given cellular neural network depends on the entity (sign and module) of the interactions existing between the cells, hence it (the system) is programmable only if it is possible to vary, during the functioning phase, the values of the interactions.
The realization of two-dimensional cellular neural networks takes advantage of the planar typology of the system itself, and this is why it can be implemented using electronic and optoelectronic technology.
Cellular neural networks are generally based on conventional VLSI electronic techniques, and in particular on the CMOS technology.
The sole solution which envisages an integrated optical input on the device is that of Espejo et al (IEEE Journal of Solid-State Circuits, SSC-29(8), 895-905 (1994)).
However, none of the aforementioned systems makes use of an optical output and/or the reconfigurability (control) by means of optical signals.
As regards programmability, the known systems when they envisage such, furnish only the possibility of realizing a discrete series of values for the connection weights between the cells. Programming is always carried out by means of electrical signals, and normally each cell should be prorammed (controlled) in an identical manner to the others.
The possible use of hydrogenated amorphous or polycrystalline silicon was recently studied by Beccherelli et al and by the present inventors, Balsi et al.
The first solution does not contain the project for a complete cellular neural network but only the study of a possible implementative model of a single cell and an interaction mechanism with a similar other such cell. This solution in no way allows programmability during the network's functioning phase, neither does it allow the realization of a general CNN, as the interaction between the cells can only be positive and without gain.
The second solution concerns the preliminary plan for a general CNN, with optical input and output but which is not programmable, as the interactions are defined in absolute values by the relative dimensions of the transistors used, and as to the sign by the type of circuit connection.